Abstract
The state of drowsiness can be characterised as an intermediate state of mind which occurs between the alert state and the sleep state. The alertness of the mind is reflected immediately through the sense organs and other body parts. Automatic detection and analysis of drowsy state of mind is essential in applications where the human’s mental status is important. One such scenario is monitoring driver’s alertness while he is driving. A multi-modal approach is analysed for detecting the drowsy state in humans. Two modalities are considered here, video information and bio signals, for analysis. Visual information conveys a lot about the human alertness. The precise indicators from the video information need to be identified and captured for analysis and detection. The bio signal that indicates human brain alertness is EEG signal. The physical and mental alertness are analysed for detecting drowsiness state of a human being. A framework is proposed for drowsiness detection of humans in real-time.
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References
NHTSA—National Highway Traffic Safety Administration, Washington DC, Online: http://www.nhtsa.gov/Driving+Safety/Drowsy+Driving
Abtahi S, Hariri B, Shirmohammadi S (2011) Driver drowsiness monitoring based on yawning detection. In: 2011 IEEE international instrumentation and measurement technology conference, Binjiang, pp 1–4
Viola P, Jones M (2001) Rapid object detection using a boosted cascade of simple features. In: Proceedings of the IEEE computer society conference on computer vision and pattern recognition CVPR, pp I-511–I-518
Azim T, Jaffar M, Mirza A (2009) Automatic fatigue detection of drivers through pupil detection and yawning analysis. In: Fourth International Conference on Innovative Computing, Information and Control, pp 441–445
Yufeng U, Zengcai W (2007) Detecting driver yawning in successive images. In: International Conference on Bioinformatics and Biomedical Engineering, pp 581–583
Saradadevi M, Bajaj P (2006) Driver fatigue detection using mouth and yawning analysis. IJCSNS Int J Comput Sci Netw Secur 8(6):183–188
Fan X, Yin B, Sun Y (2007) Yawning detection for monitoring fatigue. In: Sixth international conference on machine learning and cybernetics, Hong Kong, pp 664–668
Rigney DR, Goldberger AL, Ocasio WC, Ichimaru Y, Moody GB, Mark RG (1993) Multi-channel physiological data: description and analysis. In: Weigend AS, Gershenfeld NA (eds) Time series prediction: forecasting the future and understanding the past. Addison-Wesley, Reading, MA, pp 105–129
Ichimaru Y, Moody GB (1999) Development of the polysomnographic database on CD-ROM. Psychiatry Clin Neurosci 53:175–177
Lee J-M, Kim D-J, Kim I-Y, Park K-S, Kim SI (2002) Detrended fluctuation analysis of EEG in sleep apnea using MIT/BIH polysomnography data. Comput Biol Med 32(1):37–47. ISSN 0010-4825
Garpestad E, Katayama H, Parker JA, Ringler J, Lilly J, Ya-suda T, Moore RH, Strauss HW, Weiss JW (1992) Stroke volume and cardiac output decrease at termination of obstructive ap-neas. J Appl Physiol 73(5):1743–1748
Ichimaru Y, Clark KP, Ringler J, Weiss WJ (1990) Effect of sleep stage on the relationship between respiration and heart rate variability. Comput Cardiol 17:657–660
Vural E, Cetin M, Ercil A, Littlewort G, Bartlett M, Movellan J (2007) Drowsy driver detection through facial movement analysis. In proceedings of the IEEE international conference on Human-computer interaction (HCI). Springer, Berlin, Heidelberg, pp 6–18
Ji Qiang, Yang Xiaojie (2002) Real-time eye, gaze, and face pose tracking for monitoring driver vigilance. Real-Time Imaging 8(5):357–377
Grace R, Steward S (2001) Drowsy driver monitor and warning system. In: First international driving symposium on human factors in driver assessment, training and vehicle design, Aug 2001, pp 64–69
Drowsydriving.org/about/facts-and-stats/ by the National Sleep Foundation
Anitha C, Venkatesha MK, Suryanarayana Adiga B (2016) A two fold expert system for yawning detection. In: Second International Conference on Intelligent Computing, Communication and Convergence (ICCC-2016). Published by Elsevier Procedia of Computer Science, Bhubaneswar, India, Jan 2016, pp 63–71
Abtahi S, Omidyeganeh M, Shirmohammadi S, Hariri B (2014) YawDD: a yawning detection dataset. In: Proceedings of ACM multimedia systems, Singapore, 19–21 Mar 2014, pp 24–28
Anitha C, Venkatesha MK, Suryanarayana Adiga B (2015) High speed face detection and tracking. Int J Soft Comput Artif Intell 3(2):84–90
Iber C, Ancoli-Israel S, Chesson A, Quan SF (2007) For the American Academy of sleep medicine. In: The AASM manual for the scoring of sleep and associated events: rules, terminology and technical specifications, 1st edn, American Academy of Sleep Medicine, Illinois, Westchester
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Anitha, C. (2019). Detection and Analysis of Drowsiness in Human Beings Using Multimodal Signals. In: Patnaik, S., Yang, XS., Tavana, M., Popentiu-Vlădicescu, F., Qiao, F. (eds) Digital Business. Lecture Notes on Data Engineering and Communications Technologies, vol 21. Springer, Cham. https://doi.org/10.1007/978-3-319-93940-7_7
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DOI: https://doi.org/10.1007/978-3-319-93940-7_7
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